| scholarly article | Q13442814 |
| P50 | author | Chun Wei Yap | Q57056600 |
| P2093 | author name string | Lita Chew | |
| Terence Ng | |||
| P2860 | cites work | A new palliative prognostic score: a first step for the staging of terminally ill cancer patients. Italian Multicenter and Study Group on Palliative Care. | Q52217841 |
| Palliative chemotherapy: historical perspective, applications, and controversies. | Q53302037 | ||
| Doc, How Much Time Do I Have? | Q73385004 | ||
| Clinical survival predictors in patients with advanced cancer | Q73593004 | ||
| Multivariable analysis of prognostic factors for toxicity and survival for patients enrolled in phase I clinical trials | Q73668726 | ||
| Diagnostic accuracy of the palliative prognostic score in hospitalized patients with advanced cancer | Q81073401 | ||
| Prognostic factors among cancer patients with good performance status screened for phase I trials | Q81343760 | ||
| A systematic review of physicians' survival predictions in terminally ill cancer patients | Q24680291 | ||
| How accurate are physicians' clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients? A systematic review. | Q31961733 | ||
| Prognostic factors in patients with recently diagnosed incurable cancer: a systematic review | Q33371241 | ||
| Assessing the generalizability of prognostic information | Q33540894 | ||
| A software tool for determination of breast cancer treatment methods using data mining approach | Q33655316 | ||
| Prediction of individual patient outcome in cancer: comparison of artificial neural networks and Kaplan--Meier methods | Q34223380 | ||
| An inflammation-based prognostic score (mGPS) predicts cancer survival independent of tumour site: a Glasgow Inflammation Outcome Study. | Q34627925 | ||
| Prediction of survival in patients with esophageal carcinoma using artificial neural networks | Q36063463 | ||
| Prognostic factors in advanced cancer patients: evidence-based clinical recommendations--a study by the Steering Committee of the European Association for Palliative Care | Q36246191 | ||
| Treatment options in end-of-life care: the role of palliative chemotherapy. | Q37029886 | ||
| Extent and determinants of error in doctors' prognoses in terminally ill patients: prospective cohort study | Q37310145 | ||
| Role of systemic inflammatory response in predicting survival in patients with primary operable cancer. | Q37658782 | ||
| Neural network and regression predictions of 5-year survival after colon carcinoma treatment | Q39121212 | ||
| Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. | Q39876339 | ||
| Prognostic factors and predictive model in patients with advanced biliary tract adenocarcinoma receiving first-line palliative chemotherapy | Q39970084 | ||
| Clinical determinants of survival in patients with 5-fluorouracil-based treatment for metastatic colorectal cancer: results of a multivariate analysis of 3825 patients | Q43910377 | ||
| Survival prediction in terminally ill cancer patients by clinical estimates, laboratory tests, and self-rated anxiety and depression | Q48090050 | ||
| Artificial neural network: predicted vs observed survival in patients with colonic cancer. | Q51926424 | ||
| P433 | issue | 8 | |
| P921 | main subject | decision support system | Q330268 |
| chemotherapy | Q974135 | ||
| P304 | page(s) | 863-869 | |
| P577 | publication date | 2012-06-12 | |
| P1433 | published in | Journal of Palliative Medicine | Q6295711 |
| P1476 | title | A clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy | |
| P478 | volume | 15 |
| Q30671533 | Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT. |
| Q90254268 | Artificial intelligence in healthcare |
| Q36357410 | Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review |
| Q35393861 | Mining disease risk patterns from nationwide clinical databases for the assessment of early rheumatoid arthritis risk |
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